Skip to main content
SpringerLink
Log in
Book cover

European Conference on Information Retrieval

ECIR 2022: Advances in Information Retrieval pp 463–472Cite as

An Entity-Oriented Approach for Answering Topical Information Needs

  • Conference paper
  • First Online:

  • 2422 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13186))

Abstract

In this dissertation, we adopt an entity-oriented approach to identify relevant materials for answering a topical keyword query such as “Cholera”. To this end, we study the interplay between text and entities by addressing three related prediction problems: (1) Identify knowledge base entities that are relevant for the query, (2) Understand an entity’s meaning in the context of the query, and (3) Identify text passages that elaborate the connection between the query and an entity. Through this dissertation, we aim to study some overarching questions in entity-oriented research such as the importance of query-specific entity descriptions, and the importance of entity salience and context-dependent entity similarity for modeling the query-specific context of an entity.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    We use the salience detection system from Ponza et al. [27].

  2. 2.

    Available from the aspect catalog from Ramsdell et al.

  3. 3.

    Paper under review.

References

  1. Balog, K., Azzopardi, L., de Rijke, M.: Formal models for expert finding in enterprise corpora. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 43–50. Association for Computing Machinery, New York (2006). https://doi.org/10.1145/1148170.1148181

  2. Balog, K., Bron, M., De Rijke, M.: Query modeling for entity search based on terms, categories, and examples. ACM Trans. Inf. Syst. 29(4) (2011). https://doi.org/10.1145/2037661.2037667

  3. Banerjee, S., Mitra, P.: WikiKreator: improving Wikipedia stubs automatically. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, pp. 867–877. Association for Computational Linguistics, July 2015. https://doi.org/10.3115/v1/P15-1084. https://aclanthology.org/P15-1084

  4. Bhatia, S., Dwivedi, P., Kaur, A.: That’s interesting, tell me more! Finding descriptive support passages for knowledge graph relationships. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 250–267. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_15

    Chapter  Google Scholar 

  5. Blanco, R., Zaragoza, H.: Finding support sentences for entities. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 339–346. Association for Computing Machinery, New York (2010). https://doi.org/10.1145/1835449.1835507

  6. Chatterjee, S., Dietz, L.: Why does this entity matter? Support passage retrieval for entity retrieval. In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2019, pp. 221–224. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3341981.3344243

  7. Chatterjee, S., Dietz, L.: Entity retrieval using fine-grained entity aspects. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3404835.3463035

  8. Chen, J., Xiong, C., Callan, J.: An empirical study of learning to rank for entity search. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, pp. 737–740. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2911451.2914725

  9. Conrad, J.G., Utt, M.H.: A system for discovering relationships by feature extraction from text databases. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 260–270. Springer, London (1994). https://doi.org/10.1007/978-1-4471-2099-5_27

    Chapter  Google Scholar 

  10. Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, pp. 365–374. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2600428.2609628

  11. Dietz, L.: ENT rank: retrieving entities for topical information needs through entity-neighbor-text relations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 215–224. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3331184.3331257

  12. Dietz, L., Foley, J.: TREC CAR Y3: complex answer retrieval overview. In: Proceedings of Text REtrieval Conference (TREC) (2019)

    Google Scholar 

  13. Ensan, F., Bagheri, E.: Document retrieval model through semantic linking. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, WSDM 2017, pp. 181–190. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3018661.3018692

  14. Fetahu, B., Markert, K., Anand, A.: Automated news suggestions for populating wikipedia entity pages. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 323–332. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2806416.2806531

  15. Graus, D., Tsagkias, M., Weerkamp, W., Meij, E., de Rijke, M.: Dynamic collective entity representations for entity ranking. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, WSDM 2016, pp. 595–604. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2835776.2835819

  16. Hasibi, F., Balog, K., Bratsberg, S.E.: Exploiting entity linking in queries for entity retrieval. In: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, ICTIR 2016, pp. 209–218. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2970398.2970406

  17. Hasibi, F., et al.: DBpedia-entity V2: a test collection for entity search. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, pp. 1265–1268. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3077136.3080751

  18. Kadry, A., Dietz, L.: Open relation extraction for support passage retrieval: merit and open issues. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, pp. 1149–1152. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3077136.3080744

  19. Kaptein, R., Serdyukov, P., De Vries, A., Kamps, J.: Entity ranking using wikipedia as a pivot. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 69–78. Association for Computing Machinery, New York (2010). https://doi.org/10.1145/1871437.1871451

  20. Liu, X., Fang, H.: Latent entity space: a novel retrieval approach for entity-bearing queries. Inf. Retr. J. 18(6), 473–503 (2015)

    Article  MathSciNet  Google Scholar 

  21. Manotumruksa, J., Dalton, J., Meij, E., Yilmaz, E.: CrossBERT: a triplet neural architecture for ranking entity properties. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, pp. 2049–2052. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3397271.3401265

  22. Meij, E., Bron, M., Hollink, L., Huurnink, B., de Rijke, M.: Mapping queries to the linking open data cloud: a case study using DBpedia. J. Web Semant. 9(4), 418–433 (2011). https://doi.org/10.1016/j.websem.2011.04.001. http://www.sciencedirect.com/science/article/pii/S1570826811000187. jWS special issue on Semantic Search

  23. Metzler, D., Croft, W.B.: A Markov random field model for term dependencies. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 472–479. Association for Computing Machinery, New York (2005). https://doi.org/10.1145/1076034.1076115

  24. Nanni, F., Ponzetto, S.P., Dietz, L.: Entity-aspect linking: providing fine-grained semantics of entities in context. In: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, JCDL 2018, pp. 49–58. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3197026.3197047

  25. Nikolaev, F., Kotov, A., Zhiltsov, N.: Parameterized fielded term dependence models for ad-hoc entity retrieval from knowledge graph. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, pp. 435–444. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2911451.2911545

  26. Pirrò, G.: Explaining and suggesting relatedness in knowledge graphs. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 622–639. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_36

    Chapter  Google Scholar 

  27. Ponza, M., Ferragina, P., Piccinno, F.: SWAT: a system for detecting salient Wikipedia entities in texts. Comput. Intell. (2018). https://doi.org/10.1111/coin.12216

  28. Raghavan, H., Allan, J., McCallum, A.: An exploration of entity models, collective classification and relation description. In: KDD Workshop on Link Analysis and Group Detection, pp. 1–10 (2004)

    Google Scholar 

  29. Ramsdell, J., Dietz, L.: A large test collection for entity aspect linking. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, CIKM 2020, pp. 3109–3116. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3340531.3412875

  30. Schuhmacher, M., Dietz, L., Paolo Ponzetto, S.: Ranking entities for web queries through text and knowledge. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 1461–1470. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2806416.2806480

  31. Voskarides, N., Meij, E., de Rijke, M.: Generating descriptions of entity relationships. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 317–330. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_25

    Chapter  Google Scholar 

  32. Voskarides, N., Meij, E., Tsagkias, M., de Rijke, M., Weerkamp, W.: Learning to explain entity relationships in knowledge graphs. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, pp. 564–574. Association for Computational Linguistics, July 2015. https://doi.org/10.3115/v1/P15-1055. https://www.aclweb.org/anthology/P15-1055

  33. Xiong, C., Callan, J.: Query expansion with freebase. In: Proceedings of the 2015 International Conference on The Theory of Information Retrieval, ICTIR 2015, pp. 111–120. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2808194.2809446

  34. Xiong, C., Callan, J., Liu, T.Y.: Bag-of-entities representation for ranking. In: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, ICTIR 2016, pp. 181–184. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2970398.2970423

  35. Xiong, C., Callan, J., Liu, T.Y.: Word-entity duet representations for document ranking. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, pp. 763–772. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3077136.3080768

  36. Zhiltsov, N., Kotov, A., Nikolaev, F.: Fielded sequential dependence model for ad-hoc entity retrieval in the web of data. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015, pp. 253–262. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2766462.2767756

Download references

Author information

Authors and Affiliations

  1. University of New Hampshire, Durham, USA

    Shubham Chatterjee

Authors
  1. Shubham Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shubham Chatterjee .

Editor information

Editors and Affiliations

  1. Martin Luther University Halle-Wittenberg, Halle, Germany

    Matthias Hagen

  2. Leiden University, Leiden, The Netherlands

    Suzan Verberne

  3. University of Glasgow, Glasgow, UK

    Craig Macdonald

  4. University of Duisburg-Essen, Essen, Germany

    Christin Seifert

  5. University of Stavanger, Stavanger, Norway

    Krisztian Balog

  6. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  7. University of Stavanger, Stavanger, Norway

    Vinay Setty

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chatterjee, S. (2022). An Entity-Oriented Approach for Answering Topical Information Needs. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-99739-7_57

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99738-0

  • Online ISBN: 978-3-030-99739-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation